Leveraging the compositional nature of our world to expedite learning and facilitate
generalization is a hallmark of human perception. In machine learning, on the other hand …

We tackle the problems of latent variables identification and" out-of-support''image
generation in representation learning. We show that both are possible for a class of …

Learning structured representations of the visual world in terms of objects promises to
significantly improve the generalization abilities of current machine learning models. While …

Unsupervised learning of latent variable models (LVMs) is widely used to represent data in
machine learning. When such model reflects the ground truth factors and the mechanisms …

Identifying the causal variables of an environment and how to intervene on them is of core
value in applications such as robotics and embodied AI. While an agent can commonly …

We present a general causal generative modelling framework for accurate estimation of high
fidelity image counterfactuals with deep structural causal models. Estimation of …

Deep generative models have shown tremendous success in data density estimation and
data generation from finite samples. While these models have shown impressive …

Causal representation learning is the task of identifying the underlying causal variables and
their relations from high-dimensional observations, such as images. Recent work has shown …

Counterfactuals can offer valuable insights by answering what would have been observed
under altered circumstances, conditional on a factual observation. Whereas the classical …

Independent Mechanism Analysis (IMA) seeks to address non-identifiability in nonlinear
Independent Component Analysis (ICA) by assuming that the Jacobian of the mixing …